DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/24/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
101 Rejection Arguments
Applicant asserts:
Applicant asserts, on page 2, that the claims provide a technical solution, which provides improvements to a computing system to which it is applied. Specifically, a computer tool is needed to predict future transactions at a level of accuracy that allows for a detailed display of predicative cash-flow insights. In addition, the use of machine learning to account different data types for an improved performance.
Examiner response:
Examiner respectfully disagrees. Examiner notes that the argument for an improvement in the functioning of a computer, or an improvement to other technology or technical field is not persuasive because it is a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art.
Applicant asserts:
Applicant asserts, on page 4, that the claims do not recite an abstract idea. Specifically pointing to the use of a model selector and a plurality of machine learning model configured to predict future events for a particular type of data transfer to predict future data transfers and data transfer trends associated with the one or more data records based on the particular type of transfer and using the one of the plurality of machine learning models cannot be performed within the human mind.
Examiner response:
Examiner respectfully disagrees. Examiner notes that the use of model selector and plurality of machine learning models would fall under additional elements that is merely reciting words to “apply it” with the judicial exception. The act of predicting future events for a particular type of data transfer to predict future data transfers and data transfer trends associated with the one or more data records based on the particular type of transfer is not claimed in a way that suggests that a human could not perform this action mentally. The cited paragraph only shows an example of what the transfer data could be so that a human could not mentally assess the data and predict future events.
Applicant asserts:
Applicant asserts, on page 4, that the invention addresses a technical solution where prior system are not able to accurately and efficiently analyze and predict based on different types of data, which leads to inefficient use of computing resources in addition to achieving less accuracy. Specifically, the claimed improvement was found to provide for “faster searching of data than with the relational model”, “more efficient storage”, and “more flexibility in configuring the database”.
Examiner response:
Examiner respectfully disagrees. Examiner notes that the argument for an improvement in the functioning of a computer, or an improvement to other technology or technical field is not persuasive because it is a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art.
Applicant asserts:
Applicant asserts, on page 8, that the claims recite significantly more. Specifically, the use of the model selector and a plurality of machine learning models to address a technical solution.
Examiner response:
Examiner respectfully disagrees. Examiner notes that the use of model selector and plurality of machine learning models would fall under additional elements that is merely reciting words to “apply it” with the judicial exception. The use of a model selector does not clearly differentiate from how a human would select a model. A person and model selector could simply pick a model in any manner. The machine learning models are not clearly defined such that the predications could not be performed by a mental model of a person.
103 Rejection Arguments
Applicant asserts:
Applicant asserts, on page 9, that the prior art does not teach the amended claims
Examiner response:
Applicant’s arguments with respect to claim(s) 1-3, 8, 12-14, 19 and 24 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-22 and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In reference to claim 1:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“dynamically determine from the predicted data transfer trends of the selected machine learning model and the historical data transfers, prior factors historically influencing data transfers and thereby a set of factors likely to influence the predicted future data transfers;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine prior factors historically influencing data transfers from the predicted data transfer trends.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A computer system for presenting actionable icons on a graphical user interface (GUI) of a computer device, the computer system comprising: at least one processor; and a memory in communication with the at least one processor, the memory storing instructions, that when executed by the at least one processor, configure the system to:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“track electronic data transfers comprising interactions between one or more data records and data transfer attributes comprising types of data transfers and associated computing devices performing the data transfers;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“provide the electronic data transfers and attributes to a predictive machine learning system having a model selector and a plurality of machine learning model each trained and configured for predicting a future event for a particular type of data transfer to predict future data transfers and data transfer trends associated with the one or more data records based on the particular type of transfer and using the associated one of the plurality of machine learning models,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“the models being trained on prior historical data transfers comprising historical changes to the data records and historical data transfer attributes;”
“automatically trigger a digital nudge sent to the computer device, via a communications network, to automatically present the predicted future data transfers and the set of factors likely to influence the predicted future data transfers as one or more interactive visual insight icons on the graphical user interface for subsequent engagement;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and, responsive to a determination of engagement with the one or more interactive visual insight icons on the computer device, generating one or more action icons on the graphical user interface of the computer device, the action icons customized to adjusting the predicted future data transfers and the data transfer trends.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A computer system for presenting actionable icons on a graphical user interface (GUI) of a computer device, the computer system comprising: at least one processor; and a memory in communication with the at least one processor, the memory storing instructions, that when executed by the at least one processor, configure the system to:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“track electronic data transfers comprising interactions between one or more data records and data transfer attributes comprising types of data transfers and associated computing devices performing the data transfers;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“provide the electronic data transfers and attributes to a predictive machine learning system having a model selector and a plurality of machine learning model each trained and configured for predicting a future event for a particular type of data transfer to predict future data transfers and data transfer trends associated with the one or more data records based on the particular type of transfer and using the associated one of the plurality of machine learning models,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“the models being trained on prior historical data transfers comprising historical changes to the data records and historical data transfer attributes;”
“automatically trigger a digital nudge sent to the computer device, via a communications network, to automatically present the predicted future data transfers and the set of factors likely to influence the predicted future data transfers as one or more interactive visual insight icons on the graphical user interface for subsequent engagement;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and, responsive to a determination of engagement with the one or more interactive visual insight icons on the computer device, generating one or more action icons on the graphical user interface of the computer device, the action icons customized to adjusting the predicted future data transfers and the data transfer trends.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 4:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“determining whether a positive response indicating the visual insight icons comprising a set of data transfer insights as helpful; a neutral response indicating the visual insight icons comprising the data transfer insights were not interacted with; or a negative response indicating the visual insight icons comprising the data transfer insights were unhelpful was received on the graphical user interface of the computer device.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine if response is positive, neutral, or negative based on the feedback.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The system of claim 1, wherein a determination of engagement, further comprises: tracking feedback input received on the graphical user interface of the computer device comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The system of claim 1, wherein a determination of engagement, further comprises: tracking feedback input received on the graphical user interface of the computer device comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 6:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“determine, from the database, similar users based on the profile attributes of the user;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine similar users based on profile attributes of the user.
“grouping the similar users into clusters on the database;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could group the similar users into clusters.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The system of claim 5, wherein presenting the visual insight icons further comprises the instructions configuring the system to: dynamically generate content for the visual insight icons further based on: extracting, from a database, profile attributes of a user associated with the electronic data transfers for the one or more data records;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and responsive to said grouping, generate similar content for the visual insight icons for the similar users based on historical knowledge of insights having a positive rating by prior users.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The system of claim 5, wherein presenting the visual insight icons further comprises the instructions configuring the system to: dynamically generate content for the visual insight icons further based on: extracting, from a database, profile attributes of a user associated with the electronic data transfers for the one or more data records;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and responsive to said grouping, generate similar content for the visual insight icons for the similar users based on historical knowledge of insights having a positive rating by prior users.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 7:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The system of claim 6, wherein the instructions further configure the system to: determine, from the predictive machine learning model, a degree of confidence related to the predicted future data transfers” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine a degree of confidence related to the predicted future data transfers.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“and retrieve a set of corresponding templates from the database for presenting the one or more interactive visual insight icons based on the degree of confidence wherein each of the templates is defined as associated with a range of degree of confidence.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and retrieve a set of corresponding templates from the database for presenting the one or more interactive visual insight icons based on the degree of confidence wherein each of the templates is defined as associated with a range of degree of confidence.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 8:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The system of claim 1 wherein generating the one or more action icons further comprises the instructions configuring the system to: dynamically determine one or more potential actions relating to interacting with the one or more data records to improve the predicted future data transfers associated with the one or more data records,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine one or more potential actions relating to interacting with the one or more data records to improve the predicted future data transfers.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“and present the actions as content for the action icons to graphical user interface of the computing device.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and present the actions as content for the action icons to graphical user interface of the computing device.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 9:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The system of claim 1, wherein the instructions further configure the system to: select, using the model selector, a first trained neural network from a plurality of trained neural networks to predict the future data transfers based on the data transfer attributes,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could select a first trained neural network based on the data transfer attributes.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“wherein different predicted future data transfers are predicted in association with a particular category of data transfer attributes.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“wherein different predicted future data transfers are predicted in association with a particular category of data transfer attributes.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
In reference to claim 11:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The system of claim 1, wherein the instructions further configure the system to: determine a confidence score for the set of factors likely to influence the predicted future data transfers;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could _.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“and dynamically manage and adjust a presentation of insights presented in the one or more interactive visual insight icons on the graphical user interface based on the determined confidence score of the set of factors.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and dynamically manage and adjust a presentation of insights presented in the one or more interactive visual insight icons on the graphical user interface based on the determined confidence score of the set of factors.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 12:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“dynamically determining from the predicted data transfer trends of the selected machine learning model and the historical data transfers, prior factors historically influencing data transfers and thereby a set of factors likely to influence the predicted future data transfers;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine prior factors historically influencing data transfers from the predicted data transfer trends.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A computer implemented method for presenting actionable icons on a graphical user interface (GUI) of a computer device, the method comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“tracking electronic data transfers comprising interactions between one or more data records and data transfer attributes comprising types of data transfers and associated computing devices performing the data transfers;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“providing the electronic data transfers and attributes to a predictive machine learning system having a model selector and a plurality of machine learning models each trained and configured for predicating a future event for a particular type of data transfer to predict future data transfers and data transfer trends associated with the one or more data records based on the particular type of transfer and using the associated one of the plurality of machine learning models,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“the model being trained on prior historical data transfers comprising historical changes to the data records and historical data transfer attributes;”
“automatically triggering a digital nudge sent to the computer device, via a communications network, to automatically present the predicted future data transfers and the set of factors likely to influence the predicted future data transfers as one or more interactive visual insight icons on the graphical user interface for subsequent engagement;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and, responsive to a determination of engagement with the one or more interactive visual insight icons on the computer device, the processor triggering generating one or more action icons on the graphical user interface of the computer device, the action icons customized to adjusting the predicted future data transfers and the data transfer trends.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A computer implemented method for presenting actionable icons on a graphical user interface (GUI) of a computer device, the method comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“tracking electronic data transfers comprising interactions between one or more data records and data transfer attributes comprising types of data transfers and associated computing devices performing the data transfers;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“providing the electronic data transfers and attributes to a predictive machine learning system having a model selector and a plurality of machine learning models each trained and configured for predicating a future event for a particular type of data transfer to predict future data transfers and data transfer trends associated with the one or more data records based on the particular type of transfer and using the associated one of the plurality of machine learning models,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“the model being trained on prior historical data transfers comprising historical changes to the data records and historical data transfer attributes;”
“automatically triggering a digital nudge sent to the computer device, via a communications network, to automatically present the predicted future data transfers and the set of factors likely to influence the predicted future data transfers as one or more interactive visual insight icons on the graphical user interface for subsequent engagement;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and, responsive to a determination of engagement with the one or more interactive visual insight icons on the computer device, the processor triggering generating one or more action icons on the graphical user interface of the computer device, the action icons customized to adjusting the predicted future data transfers and the data transfer trends.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 15:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“determining whether a positive response indicating the visual insight icons comprising a set of data transfer insights as helpful; a neutral response indicating the visual insight icons comprising the data transfer insights were not interacted with; or a negative response indicating the visual insight icons comprising the data transfer insights were unhelpful was received on the graphical user interface of the computer device.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine if response is positive, neutral, or negative based on the feedback.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The method of claim 12, wherein a determination of engagement, further comprises: tracking feedback input received on the graphical user interface of the computer device comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The method of claim 12, wherein a determination of engagement, further comprises: tracking feedback input received on the graphical user interface of the computer device comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 17:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“determining, from the database, similar users based on the profile attributes of the user;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine similar users based on profile attributes of the user.
“grouping the similar users into clusters on the database;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could group the similar users into clusters.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The method of claim 16, wherein presenting the visual insight icons further comprises: dynamically generating content for the visual insight icons further based on: extracting, from a database, profile attributes of a user associated with the electronic data transfers for the one or more data records;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and responsive to said grouping, generating similar content for the visual insight icons for the similar users based on historical knowledge of insights having a positive rating by prior users.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The method of claim 16, wherein presenting the visual insight icons further comprises: dynamically generating content for the visual insight icons further based on: extracting, from a database, profile attributes of a user associated with the electronic data transfers for the one or more data records;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and responsive to said grouping, generating similar content for the visual insight icons for the similar users based on historical knowledge of insights having a positive rating by prior users.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 18:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 17 further comprising: determining, from the predictive machine learning model, a degree of confidence related to the predicted future data transfers” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine a degree of confidence related to the predicted future data transfers.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“and retrieving a set of corresponding templates from the database for presenting the one or more interactive visual insight icons based on the degree of confidence wherein each of the templates is defined as associated with a range of degree of confidence.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and retrieving a set of corresponding templates from the database for presenting the one or more interactive visual insight icons based on the degree of confidence wherein each of the templates is defined as associated with a range of degree of confidence.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 19:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 12 wherein generating the one or more action icons further comprises: dynamically determining one or more potential actions relating to interacting with the one or more data records to improve the predicted future data transfers associated with the one or more data records,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine one or more potential actions relating to interacting with the one or more data records to improve the predicted future data transfers.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“and presenting the actions as content for the action icons to graphical user interface of the computing device.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and presenting the actions as content for the action icons to graphical user interface of the computing device.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 20:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 12 further comprising: selecting, using the model selector, a first trained neural network from a plurality of trained neural networks to predict the future data transfers based on the data transfer attributes,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could select a first trained neural network based on the data transfer attributes.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“wherein different predicted future data transfers are predicted in association with a particular category of data transfer attributes.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“wherein different predicted future data transfers are predicted in association with a particular category of data transfer attributes.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 22:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 12, further comprising: determining a confidence score for the set of factors likely to influence the predicted future data transfers;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could _.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“and dynamically managing and adjusting a presentation of insights presented in the one or more interactive visual insight icons on the graphical user interface based on the determined confidence score of the set of factors.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and dynamically managing and adjusting a presentation of insights presented in the one or more interactive visual insight icons on the graphical user interface based on the determined confidence score of the set of factors.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 24:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“dynamically determining from the predicted data transfer trends of the selected machine learning model and the historical data transfers, prior factors historically influencing data transfers and thereby a set of factors likely to influence the predicted future data transfers;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine prior factors historically influencing data transfers from the predicted data transfer trends.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A tangible, non-transitory computer-readable medium storing instructions that, when executed by at least one processor, perform a method for presenting actionable icons on a graphical user interface (GUI) of a computer device comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“tracking electronic data transfers comprising interactions between one or more data records and data transfer attributes comprising types of data transfers and associated computing devices performing the data transfers;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“providing the electronic data transfers and attributes to a predictive machine learning system having a model selector and a plurality of machine learning models each trained and configured for predicating a future event for a particular type of data transfer to predict future data transfers and data transfer and data transfer trends associated with the one or more data records based on the particular type of transfer and using the associated one of the plurality of machine learning models” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“the model being trained on prior historical data transfers comprising historical changes to the data records and historical data transfer attributes;”
“automatically triggering a digital nudge to the computer device, across a communications network, to automatically present the predicted future data transfers and the set of factors likely to influence the predicted future data transfers as one or more interactive visual insight icons on the graphical user interface for subsequent engagement;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and, responsive to a determination of engagement with the one or more interactive visual insight icons on the computer device, the processor triggering generating one or more action icons on the graphical user interface of the computer device, the action icons customized to adjusting the predicted future data transfers and the data transfer trends.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A tangible, non-transitory computer-readable medium storing instructions that, when executed by at least one processor, perform a method for presenting actionable icons on a graphical user interface (GUI) of a computer device comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“tracking electronic data transfers comprising interactions between one or more data records and data transfer attributes comprising types of data transfers and associated computing devices performing the data transfers;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“providing the electronic data transfers and attributes to a predictive machine learning model having at least one neural network to predict future data transfers and data transfer trends associated with the one or more data records,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“the model being trained on prior historical data transfers comprising historical changes to the data records and historical data transfer attributes;”
“automatically triggering a digital nudge to the computer device, across a communications network, to automatically present the predicted future data transfers and the set of factors likely to influence the predicted future data transfers as one or more interactive visual insight icons on the graphical user interface for subsequent engagement;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and, responsive to a determination of engagement with the one or more interactive visual insight icons on the computer device, the processor triggering generating one or more action icons on the graphical user interface of the computer device, the action icons customized to adjusting the predicted future data transfers and the data transfer trends.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 8, 12-14, 19, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Austin et al; US 20210049668 A1 (hereinafter “Austin”) in view of Pankti Jayesh Kansara et al; US 20210117995 A1 (hereinafter “Kansara”)
Regarding claim 1, Austin teaches A computer system for presenting actionable icons on a graphical user interface (GUI) of a computer device, the computer system comprising: at least one processor; and a memory in communication with the at least one processor, the memory storing instructions, that when executed by the at least one processor, configure the system to: (Austin Paragraph 0007; "The system may include at least one memory unit storing instructions and at least one processor configured to execute the instructions to perform operations." Examiner notes that processor performs actions claimed and Fig 6 shows actionable icons)
track electronic data transfers comprising interactions between one or more data records and data transfer attributes comprising types of data transfers and associated computing devices performing the data transfers; (Austin Paragraph 0051; "At step 710, financial service provider device 130 may receive user shopping data based on a plurality of user shopping purchases over a finite time period. Financial service provider device 130 may receive data from user device 110 and/or merchant device 120 related to a user transaction over network 140." Examiner notes that service provider is tracking the received user shopping data/electronic data transfer between one or more data records/users and merchants and data transfer attributes comprising types of data transfers/pre transaction, transaction, and post transaction data and associated computing devices performing the data transfers/user and merchant device)
provide the electronic data transfers and attributes to a predictive machine learning system [having a model selector and a plurality of machine learning models each trained and configured for predicating a future event for a particular type of data transfer] to predict future data transfers and data transfer trends associated with the one or more data records [based on the particular type of transfer and using the associated one of the plurality of machine learning models], (Austin Paragraph 0055; "In some embodiments, a temporal, geographic, or other type of trigger may be determined based on at least one of Bayesian networks, Bayesian optimization, a Bayesian model, or artificial machine learning. A temporal, geographic, or other type of trigger may also be determined by extrapolating data from a directed graph and determining user 112 spending patterns based on the extrapolated data." Examiner notes that the user spending data/electronic data transfers and attributes is provided to a predictive machine learning model having at least one neural network to predict future data transfers and data transfer trends/determining user spending patterns associated with the one or more data records; user spending patterns/trends show future predicted data transfers)
the model being trained on prior historical data transfers comprising historical changes to the data records and historical data transfer attributes; (Austin Paragraph 0057; "Additionally, the user data may include a merchant specific purchase transaction history associated with user 112 over a period of time." Examiner notes that the model is trained on prior historical data transfers/purchase history transaction; history transaction data has historical changes to data records/transactions over time and historical data transfer attributes)
dynamically determine from the predicted data transfer trends of the selected machine learning model and the historical data transfers, prior factors historically influencing data transfers and thereby a set of factors likely to influence the predicted future data transfers; (Austin Paragraph 0055; "Model parameters may be optimized and/or hyperparameters may be tuned (hyperparameter tuning) in order to determine one or more triggers operating simultaneously or independently using an optimization technique, consistent with disclosed embodiments." Austin Paragraph 0068; "In some embodiments, financial service provider device 130 may be configured to monitor user spending and update machine-learning models by optimizing model parameters and/or hyperparameters (hyperparameter tuning) using an optimization technique, consistent with disclosed embodiments." Examiner notes that financial service provider device dynamically determines prior factors/temporal or geographic triggers historically influencing data transfers and thereby a set of factors likely to influence the predicted future data transfers from the predicted data transfer trends of the machine learning model and the historical data transfers; machine learning model takes in historical data to predict transfer trends where prior factors is determined; monitoring user spending and updating machine learning model is dynamic process)
automatically trigger a digital nudge sent to the computer device, via a communications network, to automatically present the predicted future data transfers and the set of factors likely to influence the predicted future data transfers as one or more interactive visual insight icons on the graphical user interface for subsequent engagement; (Austin Paragraph 0020; "As shown, system 100 includes a user device 110, a merchant device 120, and a financial service provider device 130, all of which are communicatively coupled by a network 140." Austin Paragraph 0058; "At step 740, financial service provider device 130 may display a message (as shown in FIG. 6) to user 112 indicating the trigger. The message may include user spending recommendations based on one or more of the identified triggers." Examiner notes that the digital nudge/message is automatically triggered to the computer device/user device across a communications network, to automatically present the predicted future data transfers/Fig 6 shows that money will be spent if users enters ice cream shop and the set of factors/ice cream shop nearby likely to influence the predicted future data transfers as one or more interactive visual insight icons on the graphical user interface for subsequent engagement/Fig 6)
and, responsive to a determination of engagement with the one or more interactive visual insight icons on the computer device, generating one or more action icons on the graphical user interface of the computer device, the action icons customized to adjusting the predicted future data transfers and the data transfer trends. (Austin Paragraph 0059; "In some aspects, the message or notification alerts user 112 of an action based on an identified trigger. For example, as shown in FIG. 6 in an embodiment in which the trigger constitutes purchases at a particular merchant, a temporary credit line freeze may be made, and financial service provider device 130 may send a notification to user device 110, which may be a mobile device, to alert user 112 of the credit freeze." Examiner notes that responsive to a determination of engagement with the one or more interactive visual insight icons on the computer device/notification alerts user of an action based on an identified trigger Fig 6 610, the processor triggering generating one or more action icons on the graphical user interface of the computer device/button to override credit card freeze Fig 6 620, the action icons customized to adjusting the predicted future data transfers and the data transfer trends/based on user overriding credit freeze or not the predicted future data transfers and trends is adjusted)
Austin does not teach having a model selector and a plurality of machine learning models each trained and configured for predicating a future event for a particular type of data transfer
to predict future data transfers and data transfer trends associated with the one or more data records based on the particular type of transfer and using the associated one of the plurality of machine learning models,
However, Kansara does teach having a model selector and a plurality of machine learning models each trained and configured for predicating a future event for a particular type of data transfer (Kansara Paragraph 28; “The transaction date modeler 126 can take as input compressed or uncompressed sequences generated by the transaction date sequencer 124, and train multiple machine learning models 128 for each of the sequences. The transaction date modeler 126 can select a best machine learning model 128 for each retailer/product hierarchy level based on heuristics. The selected machine learning model 128 can be used to generate a list of predicted transaction dates.” Examiner notes that a model selector (transaction date modeler) and a plurality of machine learning models (multiple machine learning models) each trained and configured for predicting a future event (predict transaction dates) for a particular type of data transfer (transaction between retailer/product))
based on the particular type of transfer and using the associated one of the plurality of machine learning models, (Examiner refers to previous mapping to show that prediction is based on the particular type of transfer (transaction between retailer/product) and using the associated one of the plurality of machine learning models (using selected model for appropriate transaction between retailer/product))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin and Kansara. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. One of ordinary skill would have motivation to combine Austin and Kansara to employ the predication system to increase a likelihood that a visit is a productive visit “Businesses may desire to employ strategies to increase a likelihood that a visit is a productive visit. In some cases, businesses may desire to seek improved ways to dynamically optimize salesperson routes when historical transaction data (e.g., purchases of the products by retailers) is sparse. A prediction system described herein can be used to proactively predict demand based on sparse transaction data, with the predicted demand being used, for example, to determine particular retailers to visit and at identified times.” (Kansara Paragraph 0017).
Regarding claim 2, Austin teaches The system of claim 1, wherein the data transfer attributes comprise: movement of data transfers between the one or more data records, (Austin Paragraph 0051; "At step 710, financial service provider device 130 may receive user shopping data based on a plurality of user shopping purchases over a finite time period. Financial service provider device 130 may receive data from user device 110 and/or merchant device 120 related to a user transaction over network 140." Examiner notes that the data transfer attributes comprise movement of data transfers between the one or more data records/ transactions between users and merchants)
and at least one credit and one debit posting in the one or more data records. (Austin Paragraph 0027; "The financial service provider includes infrastructure and components that are configured to generate and provide financial service accounts and financial service account cards (e.g., debit cards, credit cards)." Examiner notes that the financial service provider includes infrastructure that supports debit and credit cards indicating that there are at least one credit and one debit posting in the one or more data records)
Regarding claim 3, Austin teaches The system of claim 1, wherein triggering the presenting further comprises: trigger presenting of the set of factors alongside prior factors historically influencing the data transfers on the graphical user interface for subsequent engagement. (Austin Paragraph 0057; "The merchant trigger may or may not be based solely on geographic parameters and may instead be based primarily on the goods or services it offers for sale, which may act as a trigger to user 112." Examiner notes that presenting the set of factors/trigger and prior factors historically influencing the data transfers on the graphical user interface for subsequent engagement/Fig 6; Factors and prior factors can remain consistent and be the same)
Regarding claim 8, Austin teaches The system of claim 1 wherein generating the one or more action icons further comprises the instructions configuring the system to: dynamically determine one or more potential actions relating to interacting with the one or more data records to improve the predicted future data transfers associated with the one or more data records, (Austin Paragraph 0058; "As used herein, user 112 is located near a merchant or merchant's premises when the user is within at least one mile from the merchant's premises. User 112 may also be located near when user 112 is directly outside, one quarter mile or less, or one-half mile or less from the merchant's premises. Further, user 112 is located at the merchant or merchant's premises when user 112 is inside or within the merchant's premises. In some aspects, the message or notification alerts user 112 of an action based on an identified trigger." Austin Paragraph 0060; "as shown in FIG. 6, the notification may inform user 112 of the option to override the credit freeze." Examiner notes that generating one or more action icons/override option comprises dynamically determining/based on triggers one or more potential actions/freeze or unfreeze credit relating to interacting with the one or more data records/credit line to improve the predicted future data transfers associated with the one or more data records/frozen credit line means no transfers and unfrozen means potential transfers)
Regarding claim 12, Austin teaches A computer implemented method for presenting actionable icons on a graphical user interface (GUI) of a computer device, the method comprising: (Austin Paragraph 0007; "The system may include at least one memory unit storing instructions and at least one processor configured to execute the instructions to perform operations." Examiner notes that processor performs actions claimed and Fig 6 shows actionable icons)
tracking, by a processor of a computer system, electronic data transfers comprising interactions between one or more data records and data transfer attributes comprising types of data transfers and associated computing devices performing the data transfers; (Austin Paragraph 0051; "At step 710, financial service provider device 130 may receive user shopping data based on a plurality of user shopping purchases over a finite time period. Financial service provider device 130 may receive data from user device 110 and/or merchant device 120 related to a user transaction over network 140." Examiner notes that service provider is tracking the received user shopping data/electronic data transfer between one or more data records/users and merchants and data transfer attributes comprising types of data transfers/pre transaction, transaction, and post transaction data and associated computing devices performing the data transfers/user and merchant device)
providing, by the processor, the electronic data transfers and attributes to a predictive machine learning model having [having a model selector and a plurality of machine learning models each trained and configured for predicating a future event for a particular type of data transfer] to predict future data transfers and data transfer trends associated with the one or more data records [based on the particular type of transfer and using the associated one of the plurality of machine learning models], (Austin Paragraph 0055; "In some embodiments, a temporal, geographic, or other type of trigger may be determined based on at least one of Bayesian networks, Bayesian optimization, a Bayesian model, or artificial machine learning. A temporal, geographic, or other type of trigger may also be determined by extrapolating data from a directed graph and determining user 112 spending patterns based on the extrapolated data." Examiner notes that the user spending data/electronic data transfers and attributes is provided to a predictive machine learning model having at least one neural network to predict future data transfers and data transfer trends/determining user spending patterns associated with the one or more data records; user spending patterns/trends show future predicted data transfers)
the model being trained on prior historical data transfers comprising historical changes to the data records and historical data transfer attributes; (Austin Paragraph 0057; "Additionally, the user data may include a merchant specific purchase transaction history associated with user 112 over a period of time." Examiner notes that the model is trained on prior historical data transfers/purchase history transaction; history transaction data has historical changes to data records/transactions over time and historical data transfer attributes)
dynamically determining by the processor, from the predicted data transfer trends of the selected machine learning model and the historical data transfers, prior factors historically influencing data transfers and thereby a set of factors likely to influence the predicted future data transfers; (Austin Paragraph 0055; "Model parameters may be optimized and/or hyperparameters may be tuned (hyperparameter tuning) in order to determine one or more triggers operating simultaneously or independently using an optimization technique, consistent with disclosed embodiments." Austin Paragraph 0068; "In some embodiments, financial service provider device 130 may be configured to monitor user spending and update machine-learning models by optimizing model parameters and/or hyperparameters (hyperparameter tuning) using an optimization technique, consistent with disclosed embodiments." Examiner notes that financial service provider device dynamically determines prior factors/temporal or geographic triggers historically influencing data transfers and thereby a set of factors likely to influence the predicted future data transfers from the predicted data transfer trends of the machine learning model and the historical data transfers; machine learning model takes in historical data to predict transfer trends where prior factors is determined; monitoring user spending and updating machine learning model is dynamic process)
automatically triggering by the processor a digital nudge sent to the computer device, via a communications network, to automatically present the predicted future data transfers and the set of factors likely to influence the predicted future data transfers as one or more interactive visual insight icons on the graphical user interface for subsequent engagement; (Austin Paragraph 0020; "As shown, system 100 includes a user device 110, a merchant device 120, and a financial service provider device 130, all of which are communicatively coupled by a network 140." Austin Paragraph 0058; "At step 740, financial service provider device 130 may display a message (as shown in FIG. 6) to user 112 indicating the trigger. The message may include user spending recommendations based on one or more of the identified triggers." Examiner notes that the digital nudge/message is automatically triggered to the computer device/user device across a communications network, to automatically present the predicted future data transfers/Fig 6 shows that money will be spent if users enters ice cream shop and the set of factors/ice cream shop nearby likely to influence the predicted future data transfers as one or more interactive visual insight icons on the graphical user interface for subsequent engagement/Fig 6)
and, responsive to a determination of engagement with the one or more interactive visual insight icons on the computer device, the processor triggering generating one or more action icons on the graphical user interface of the computer device, the action icons customized to adjusting the predicted future data transfers and the data transfer trends. (Austin Paragraph 0059; "In some aspects, the message or notification alerts user 112 of an action based on an identified trigger. For example, as shown in FIG. 6 in an embodiment in which the trigger constitutes purchases at a particular merchant, a temporary credit line freeze may be made, and financial service provider device 130 may send a notification to user device 110, which may be a mobile device, to alert user 112 of the credit freeze." Examiner notes that responsive to a determination of engagement with the one or more interactive visual insight icons on the computer device/notification alerts user of an action based on an identified trigger Fig 6 610, the processor triggering generating one or more action icons on the graphical user interface of the computer device/button to override credit card freeze Fig 6 620, the action icons customized to adjusting the predicted future data transfers and the data transfer trends/based on user overriding credit freeze or not the predicted future data transfers and trends is adjusted)
Austin does not teach having a model selector and a plurality of machine learning models each trained and configured for predicating a future event for a particular type of data transfer
to predict future data transfers and data transfer trends associated with the one or more data records based on the particular type of transfer and using the associated one of the plurality of machine learning models,
However, Kansara does teach having a model selector and a plurality of machine learning models each trained and configured for predicating a future event for a particular type of data transfer (Kansara Paragraph 28; “The transaction date modeler 126 can take as input compressed or uncompressed sequences generated by the transaction date sequencer 124, and train multiple machine learning models 128 for each of the sequences. The transaction date modeler 126 can select a best machine learning model 128 for each retailer/product hierarchy level based on heuristics. The selected machine learning model 128 can be used to generate a list of predicted transaction dates.” Examiner notes that a model selector (transaction date modeler) and a plurality of machine learning models (multiple machine learning models) each trained and configured for predicting a future event (predict transaction dates) for a particular type of data transfer (transaction between retailer/product))
based on the particular type of transfer and using the associated one of the plurality of machine learning models, (Examiner refers to previous mapping to show that prediction is based on the particular type of transfer (transaction between retailer/product) and using the associated one of the plurality of machine learning models (using selected model for appropriate transaction between retailer/product))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin and Kansara. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. One of ordinary skill would have motivation to combine Austin and Kansara to employ the predication system to increase a likelihood that a visit is a productive visit “Businesses may desire to employ strategies to increase a likelihood that a visit is a productive visit. In some cases, businesses may desire to seek improved ways to dynamically optimize salesperson routes when historical transaction data (e.g., purchases of the products by retailers) is sparse. A prediction system described herein can be used to proactively predict demand based on sparse transaction data, with the predicted demand being used, for example, to determine particular retailers to visit and at identified times.” (Kansara Paragraph 0017).
Regarding claim 13, Austin teaches The method of claim 12, wherein the data transfer attributes comprise: movement of data transfers between the one or more data records, (Austin Paragraph 0051; "At step 710, financial service provider device 130 may receive user shopping data based on a plurality of user shopping purchases over a finite time period. Financial service provider device 130 may receive data from user device 110 and/or merchant device 120 related to a user transaction over network 140." Examiner notes that the data transfer attributes comprise movement of data transfers between the one or more data records/ transactions between users and merchants)
and at least one credit and one debit posting in the one or more data records. (Austin Paragraph 0027; "The financial service provider includes infrastructure and components that are configured to generate and provide financial service accounts and financial service account cards (e.g., debit cards, credit cards)." Examiner notes that the financial service provider includes infrastructure that supports debit and credit cards indicating that there are at least one credit and one debit posting in the one or more data records)
Regarding claim 14, Austin teaches The method of claim 12, wherein triggering the presenting further comprises: trigger presenting of the set of factors alongside prior factors historically influencing the data transfers on the graphical user interface for subsequent engagement. (Austin Paragraph 0057; "The merchant trigger may or may not be based solely on geographic parameters and may instead be based primarily on the goods or services it offers for sale, which may act as a trigger to user 112." Examiner notes that presenting the set of factors/trigger and prior factors historically influencing the data transfers on the graphical user interface for subsequent engagement/Fig 6; Factors and prior factors can remain consistent and be the same)
Regarding claim 19, Austin teaches The method of claim 12 wherein generating the one or more action icons further comprises: dynamically determining one or more potential actions relating to interacting with the one or more data records to improve the predicted future data transfers associated with the one or more data records, (Austin Paragraph 0058; "As used herein, user 112 is located near a merchant or merchant's premises when the user is within at least one mile from the merchant's premises. User 112 may also be located near when user 112 is directly outside, one quarter mile or less, or one-half mile or less from the merchant's premises. Further, user 112 is located at the merchant or merchant's premises when user 112 is inside or within the merchant's premises. In some aspects, the message or notification alerts user 112 of an action based on an identified trigger." Austin Paragraph 0060; "as shown in FIG. 6, the notification may inform user 112 of the option to override the credit freeze." Examiner notes that generating one or more action icons/override option comprises dynamically determining/based on triggers one or more potential actions/freeze or unfreeze credit relating to interacting with the one or more data records/credit line to improve the predicted future data transfers associated with the one or more data records/frozen credit line means no transfers and unfrozen means potential transfers)
Regarding claim 24, Austin teaches A computer implemented method for presenting actionable icons on a graphical user interface (GUI) of a computer device, the method comprising: (Austin Paragraph 0007; "The system may include at least one memory unit storing instructions and at least one processor configured to execute the instructions to perform operations." Examiner notes that processor performs actions claimed and Fig 6 shows actionable icons)
tracking, by a processor of a computer system, electronic data transfers comprising interactions between one or more data records and data transfer attributes comprising types of data transfers and associated computing devices performing the data transfers; (Austin Paragraph 0051; "At step 710, financial service provider device 130 may receive user shopping data based on a plurality of user shopping purchases over a finite time period. Financial service provider device 130 may receive data from user device 110 and/or merchant device 120 related to a user transaction over network 140." Examiner notes that service provider is tracking the received user shopping data/electronic data transfer between one or more data records/users and merchants and data transfer attributes comprising types of data transfers/pre transaction, transaction, and post transaction data and associated computing devices performing the data transfers/user and merchant device)
providing, by the processor, the electronic data transfers and attributes to a predictive machine learning model [having a model selector and a plurality of machine learning models each trained and configured for predicating a future event for a particular type of data transfer] to predict future data transfers and data transfer trends associated with the one or more data records [based on the particular type of transfer and using the associated one of the plurality of machine learning models] (Austin Paragraph 0055; "In some embodiments, a temporal, geographic, or other type of trigger may be determined based on at least one of Bayesian networks, Bayesian optimization, a Bayesian model, or artificial machine learning. A temporal, geographic, or other type of trigger may also be determined by extrapolating data from a directed graph and determining user 112 spending patterns based on the extrapolated data." Examiner notes that the user spending data/electronic data transfers and attributes is provided to a predictive machine learning model having at least one neural network to predict future data transfers and data transfer trends/determining user spending patterns associated with the one or more data records; user spending patterns/trends show future predicted data transfers)
the model being trained on prior historical data transfers comprising historical changes to the data records and historical data transfer attributes; (Austin Paragraph 0057; "Additionally, the user data may include a merchant specific purchase transaction history associated with user 112 over a period of time." Examiner notes that the model is trained on prior historical data transfers/purchase history transaction; history transaction data has historical changes to data records/transactions over time and historical data transfer attributes)
dynamically determining by the processor, from the predicted data transfer trends of the selected machine learning model and the historical data transfers, prior factors historically influencing data transfers and thereby a set of factors likely to influence the predicted future data transfers; (Austin Paragraph 0055; "Model parameters may be optimized and/or hyperparameters may be tuned (hyperparameter tuning) in order to determine one or more triggers operating simultaneously or independently using an optimization technique, consistent with disclosed embodiments." Austin Paragraph 0068; "In some embodiments, financial service provider device 130 may be configured to monitor user spending and update machine-learning models by optimizing model parameters and/or hyperparameters (hyperparameter tuning) using an optimization technique, consistent with disclosed embodiments." Examiner notes that financial service provider device dynamically determines prior factors/temporal or geographic triggers historically influencing data transfers and thereby a set of factors likely to influence the predicted future data transfers from the predicted data transfer trends of the machine learning model and the historical data transfers; machine learning model takes in historical data to predict transfer trends where prior factors is determined; monitoring user spending and updating machine learning model is dynamic process)
automatically triggering by the processor a digital nudge sent to the computer device, via a communications network, to automatically present the predicted future data transfers and the set of factors likely to influence the predicted future data transfers as one or more interactive visual insight icons on the graphical user interface for subsequent engagement; (Austin Paragraph 0020; "As shown, system 100 includes a user device 110, a merchant device 120, and a financial service provider device 130, all of which are communicatively coupled by a network 140." Austin Paragraph 0058; "At step 740, financial service provider device 130 may display a message (as shown in FIG. 6) to user 112 indicating the trigger. The message may include user spending recommendations based on one or more of the identified triggers." Examiner notes that the digital nudge/message is automatically triggered to the computer device/user device across a communications network, to automatically present the predicted future data transfers/Fig 6 shows that money will be spent if users enters ice cream shop and the set of factors/ice cream shop nearby likely to influence the predicted future data transfers as one or more interactive visual insight icons on the graphical user interface for subsequent engagement/Fig 6)
and, responsive to a determination of engagement with the one or more interactive visual insight icons on the computer device, the processor triggering generating one or more action icons on the graphical user interface of the computer device, the action icons customized to adjusting the predicted future data transfers and the data transfer trends. (Austin Paragraph 0059; "In some aspects, the message or notification alerts user 112 of an action based on an identified trigger. For example, as shown in FIG. 6 in an embodiment in which the trigger constitutes purchases at a particular merchant, a temporary credit line freeze may be made, and financial service provider device 130 may send a notification to user device 110, which may be a mobile device, to alert user 112 of the credit freeze." Examiner notes that responsive to a determination of engagement with the one or more interactive visual insight icons on the computer device/notification alerts user of an action based on an identified trigger Fig 6 610, the processor triggering generating one or more action icons on the graphical user interface of the computer device/button to override credit card freeze Fig 6 620, the action icons customized to adjusting the predicted future data transfers and the data transfer trends/based on user overriding credit freeze or not the predicted future data transfers and trends is adjusted)
Austin does not teach having a model selector and a plurality of machine learning models each trained and configured for predicating a future event for a particular type of data transfer
to predict future data transfers and data transfer trends associated with the one or more data records based on the particular type of transfer and using the associated one of the plurality of machine learning models,
However, Kansara does teach having a model selector and a plurality of machine learning models each trained and configured for predicating a future event for a particular type of data transfer (Kansara Paragraph 28; “The transaction date modeler 126 can take as input compressed or uncompressed sequences generated by the transaction date sequencer 124, and train multiple machine learning models 128 for each of the sequences. The transaction date modeler 126 can select a best machine learning model 128 for each retailer/product hierarchy level based on heuristics. The selected machine learning model 128 can be used to generate a list of predicted transaction dates.” Examiner notes that a model selector (transaction date modeler) and a plurality of machine learning models (multiple machine learning models) each trained and configured for predicting a future event (predict transaction dates) for a particular type of data transfer (transaction between retailer/product))
based on the particular type of transfer and using the associated one of the plurality of machine learning models, (Examiner refers to previous mapping to show that prediction is based on the particular type of transfer (transaction between retailer/product) and using the associated one of the plurality of machine learning models (using selected model for appropriate transaction between retailer/product))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin and Kansara. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. One of ordinary skill would have motivation to combine Austin and Kansara to employ the predication system to increase a likelihood that a visit is a productive visit “Businesses may desire to employ strategies to increase a likelihood that a visit is a productive visit. In some cases, businesses may desire to seek improved ways to dynamically optimize salesperson routes when historical transaction data (e.g., purchases of the products by retailers) is sparse. A prediction system described herein can be used to proactively predict demand based on sparse transaction data, with the predicted demand being used, for example, to determine particular retailers to visit and at identified times.” (Kansara Paragraph 0017).
Claim(s) 4, 5, 9, 10, 15, 16, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Austin et al; US 20210049668 A1 (hereinafter “Austin”) in view of Pankti Jayesh Kansara et al; US 20210117995 A1 (hereinafter “Kansara”) in further view of James et al; US 20210350385 A1 (hereinafter “James”).
Regarding claim 4, Austin does not teach The system of claim 1, wherein a determination of engagement, further comprises: tracking feedback input received on the graphical user interface of the computer device
comprising: determining whether a positive response indicating the visual insight icons comprising a set of data transfer insights as helpful; a neutral response indicating the visual insight icons comprising the data transfer insights were not interacted with; or a negative response indicating the visual insight icons comprising the data transfer insights were unhelpful was received on the graphical user interface of the computer device.
However, James does teach The system of claim 1, wherein a determination of engagement, further comprises: tracking feedback input received on the graphical user interface of the computer device (James paragraph 0055; "feedback mechanism with adjacent thumbs up/thumbs down buttons for providing feedback signals used to train and/or retrain associated ML models." Examiner notes that user feedback/determination of engagement is tracked for training and/or retraining the ML models)
comprising: determining whether a positive response indicating the visual insight icons comprising a set of data transfer insights as helpful; a neutral response indicating the visual insight icons comprising the data transfer insights were not interacted with; or a negative response indicating the visual insight icons comprising the data transfer insights were unhelpful was received on the graphical user interface of the computer device. (James paragraph 0055; "feedback mechanism with adjacent thumbs up/thumbs down buttons for providing feedback signals used to train and/or retrain associated ML models." Examiner notes that graphical user interface comprises determining a positive response/thumbs up indicating the visual insight icons comprising a set of data transfer insights as helpful and a negative response/thumbs down indicating visual insight icons comprising the data transfer insights were unhelpful)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara and James. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. One of ordinary skill would have motivation to combine Austin, and Kansara and James to use user feedback to customer service goals “However, such summaries can be inaccurate and/or omit important details including details for which feedback would be most valuable (e.g., details enabling determination of causes of failure to achieve customer service goals).” (James Paragraph 0016).
Regarding claim 5, Austin teaches and, responsive to retraining the model, trigger the graphical user interface of the computing device to display an updated visual insight icons comprising the updated set of factors. (Austin Paragraph 0068; "In some embodiments, financial service provider device 130 may be configured to monitor user spending and update machine-learning models by optimizing model parameters and/or hyperparameters (hyperparameter tuning) using an optimization technique, consistent with disclosed embodiments." Examiner notes that responsive to retraining/configured to monitor user spending and update machine learning model, triggering the graphical user interface of the computing device to display an updated visual insight icons comprising the updated set of factors/the updated factors will be reflected on the GUI as shown in Fig 6)
Austin does not teach The system of claim 4, wherein the instructions further configure the system to: transmit the feedback input to the predictive machine learning model to invoke the predictive machine learning model to revise the training of the model based on the feedback input and thereby update the set of factors;
However, James does teach The system of claim 4, wherein the instructions further configure the system to: transmit the feedback input to the predictive machine learning model to invoke the predictive machine learning model to revise the training of the model based on the feedback input and thereby update the set of factors; (James paragraph 0055; "feedback mechanism with adjacent thumbs up/thumbs down buttons for providing feedback signals used to train and/or retrain associated ML models." Examiner notes that feedback signals/transmitting the feedback input to the predictive machine learning model to invoke the predictive machine learning model to revise the training/retraining of the model based on the feedback input and thereby update the set of factors; updated ML model will generate updated factors)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara and James. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. One of ordinary skill would have motivation to combine Austin, and Kansara and James to use user feedback to customer service goals “However, such summaries can be inaccurate and/or omit important details including details for which feedback would be most valuable (e.g., details enabling determination of causes of failure to achieve customer service goals).” (James Paragraph 0016).
Regarding claim 9, Austin teaches trained neural networks to predict the future data transfers based on the data transfer attributes, (Austin Paragraph 0055; "In some embodiments, a temporal, geographic, or other type of trigger may be determined based on at least one of Bayesian networks, Bayesian optimization, a Bayesian model, or artificial machine learning. A temporal, geographic, or other type of trigger may also be determined by extrapolating data from a directed graph and determining user 112 spending patterns based on the extrapolated data." Examiner notes that artificial machine learning is the trained neural network to predict future data transfers/spending patterns based on the data transfer attributes/extrapolated data)
wherein different predicted future data transfers are predicted in association with a particular category of data transfer attributes. (Austin Paragraph 0058; "The message may include user spending recommendations based on one or more of the identified triggers. For example, financial service provider device 130 may determine that the identified user is at or near a specific merchant (e.g., ice cream shops or grocery shop) and may transmit a message or notification to user device 110." Examiner notes that once a user is near an ice cream shop or grocery shop, financial service provider predicts a transaction/future data transfers will be made and it is associated with a particular category of data transfer attributes/ice cream shop or grocery shop.)
Austin does not teach using the model selector
However, Kansara does teach using the model selector (Kansara Paragraph 28; “The transaction date modeler 126 can take as input compressed or uncompressed sequences generated by the transaction date sequencer 124, and train multiple machine learning models 128 for each of the sequences. The transaction date modeler 126 can select a best machine learning model 128 for each retailer/product hierarchy level based on heuristics.” Examiner notes that the model selector is the transaction data modeler)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin and Kansara. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. One of ordinary skill would have motivation to combine Austin and Kansara to employ the predication system to increase a likelihood that a visit is a productive visit “Businesses may desire to employ strategies to increase a likelihood that a visit is a productive visit. In some cases, businesses may desire to seek improved ways to dynamically optimize salesperson routes when historical transaction data (e.g., purchases of the products by retailers) is sparse. A prediction system described herein can be used to proactively predict demand based on sparse transaction data, with the predicted demand being used, for example, to determine particular retailers to visit and at identified times.” (Kansara Paragraph 0017).
Austin in view of Kansara does not teach The system of claim 1, wherein the instructions further configure the system to: select[, using the model selector,] a first trained neural network from a plurality of trained neural networks
However, James does teach The system of claim 1, wherein the instructions further configure the system to: select[, using the model selector,] a first trained neural network from a plurality of trained neural networks (James Paragraph 0103; "the computing devices 520 may select a model that is configured to receive text-based data." Examiner notes that selecting a model/first trained neural network means it is selected from a plurality of models to predict the future data transfers based on the data transfer attributes)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara and James. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. One of ordinary skill would have motivation to combine Austin, and Kansara and James to use user feedback to customer service goals “However, such summaries can be inaccurate and/or omit important details including details for which feedback would be most valuable (e.g., details enabling determination of causes of failure to achieve customer service goals).” (James Paragraph 0016).
Regarding claim 10, Austin teaches The system of claim 9, wherein the first trained neural network comprises at least one of: a regression model, a rule-based model, and a decision tree based ensemble model using gradient boosting framework. (Austin Paragraph 0061; "At step 750, financial service provider device 130 may add one or more spending recommendation rules to the statistical model based on direct user input." Examiner notes that the statistical model can be modified with rules making it a rule-based model)
Regarding claim 15, Austin does not teach The method of claim 12, wherein a determination of engagement, further comprises: tracking feedback input received on the graphical user interface of the computer device
comprising: determining whether a positive response indicating the visual insight icons comprising a set of data transfer insights as helpful; a neutral response indicating the visual insight icons comprising the data transfer insights were not interacted with; or a negative response indicating the visual insight icons comprising the data transfer insights were unhelpful was received on the graphical user interface of the computer device.
However, James does teach The method of claim 12, wherein a determination of engagement, further comprises: tracking feedback input received on the graphical user interface of the computer device (James paragraph 0055; "feedback mechanism with adjacent thumbs up/thumbs down buttons for providing feedback signals used to train and/or retrain associated ML models." Examiner notes that user feedback/determination of engagement is tracked for training and/or retraining the ML models)
comprising: determining whether a positive response indicating the visual insight icons comprising a set of data transfer insights as helpful; a neutral response indicating the visual insight icons comprising the data transfer insights were not interacted with; or a negative response indicating the visual insight icons comprising the data transfer insights were unhelpful was received on the graphical user interface of the computer device. (James paragraph 0055; "feedback mechanism with adjacent thumbs up/thumbs down buttons for providing feedback signals used to train and/or retrain associated ML models." Examiner notes that graphical user interface comprises determining a positive response/thumbs up indicating the visual insight icons comprising a set of data transfer insights as helpful and a negative response/thumbs down indicating visual insight icons comprising the data transfer insights were unhelpful)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara and James. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. One of ordinary skill would have motivation to combine Austin, and Kansara and James to use user feedback to customer service goals “However, such summaries can be inaccurate and/or omit important details including details for which feedback would be most valuable (e.g., details enabling determination of causes of failure to achieve customer service goals).” (James Paragraph 0016).
Regarding claim 16, Austin teaches and, responsive to retraining the model, triggering the graphical user interface of the computing device to display an updated visual insight icons comprising the updated set of factors. (Austin Paragraph 0068; "In some embodiments, financial service provider device 130 may be configured to monitor user spending and update machine-learning models by optimizing model parameters and/or hyperparameters (hyperparameter tuning) using an optimization technique, consistent with disclosed embodiments." Examiner notes that responsive to retraining/configured to monitor user spending and update machine learning model, triggering the graphical user interface of the computing device to display an updated visual insight icons comprising the updated set of factors/the updated factors will be reflected on the GUI as shown in Fig 6)
Austin does not teach The method of claim 15, further comprising: transmitting the feedback input to the predictive machine learning model to invoke the predictive machine learning model to revise the training of the model based on the feedback input and thereby update the set of factors;
However, James does teach The method of claim 15, further comprising: transmitting the feedback input to the predictive machine learning model to invoke the predictive machine learning model to revise the training of the model based on the feedback input and thereby update the set of factors; (James paragraph 0055; "feedback mechanism with adjacent thumbs up/thumbs down buttons for providing feedback signals used to train and/or retrain associated ML models." Examiner notes that feedback signals/transmitting the feedback input to the predictive machine learning model to invoke the predictive machine learning model to revise the training/retraining of the model based on the feedback input and thereby update the set of factors; updated ML model will generate updated factors)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara and James. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. One of ordinary skill would have motivation to combine Austin, and Kansara and James to use user feedback to customer service goals “However, such summaries can be inaccurate and/or omit important details including details for which feedback would be most valuable (e.g., details enabling determination of causes of failure to achieve customer service goals).” (James Paragraph 0016).
Regarding claim 20, Austin teaches trained neural networks to predict the future data transfers based on the data transfer attributes, (Austin Paragraph 0055; "In some embodiments, a temporal, geographic, or other type of trigger may be determined based on at least one of Bayesian networks, Bayesian optimization, a Bayesian model, or artificial machine learning. A temporal, geographic, or other type of trigger may also be determined by extrapolating data from a directed graph and determining user 112 spending patterns based on the extrapolated data." Examiner notes that artificial machine learning is the trained neural network to predict future data transfers/spending patterns based on the data transfer attributes/extrapolated data)
wherein different predicted future data transfers are predicted in association with a particular category of data transfer attributes. (Austin Paragraph 0058; "The message may include user spending recommendations based on one or more of the identified triggers. For example, financial service provider device 130 may determine that the identified user is at or near a specific merchant (e.g., ice cream shops or grocery shop) and may transmit a message or notification to user device 110." Examiner notes that once a user is near an ice cream shop or grocery shop, financial service provider predicts a transaction/future data transfers will be made and it is associated with a particular category of data transfer attributes/ice cream shop or grocery shop.)
Austin does not teach using the model selector
However, Kansara does teach using the model selector (Kansara Paragraph 28; “The transaction date modeler 126 can take as input compressed or uncompressed sequences generated by the transaction date sequencer 124, and train multiple machine learning models 128 for each of the sequences. The transaction date modeler 126 can select a best machine learning model 128 for each retailer/product hierarchy level based on heuristics.” Examiner notes that the model selector is the transaction data modeler)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin and Kansara. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. One of ordinary skill would have motivation to combine Austin and Kansara to employ the predication system to increase a likelihood that a visit is a productive visit “Businesses may desire to employ strategies to increase a likelihood that a visit is a productive visit. In some cases, businesses may desire to seek improved ways to dynamically optimize salesperson routes when historical transaction data (e.g., purchases of the products by retailers) is sparse. A prediction system described herein can be used to proactively predict demand based on sparse transaction data, with the predicted demand being used, for example, to determine particular retailers to visit and at identified times.” (Kansara Paragraph 0017).
Austin does not teach The method of claim 12 further comprising: selecting], using the model selector,] a first trained neural network from a plurality of trained neural networks
However, James does teach The method of claim 12 further comprising: selecting], using the model selector,] a first trained neural network from a plurality of trained neural networks (James Paragraph 0103; "the computing devices 520 may select a model that is configured to receive text-based data." Examiner notes that selecting a model/first trained neural network means it is selected from a plurality of models to predict the future data transfers based on the data transfer attributes)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara and James. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. One of ordinary skill would have motivation to combine Austin, and Kansara and James to use user feedback to customer service goals “However, such summaries can be inaccurate and/or omit important details including details for which feedback would be most valuable (e.g., details enabling determination of causes of failure to achieve customer service goals).” (James Paragraph 0016).
Regarding claim 21, Austin teaches The method of claim 20, wherein the first trained neural network comprises at least one of: a regression model, a rule-based model, and a decision tree based ensemble model using gradient boosting framework. (Austin Paragraph 0061; "At step 750, financial service provider device 130 may add one or more spending recommendation rules to the statistical model based on direct user input." Examiner notes that the statistical model can be modified with rules making it a rule-based model)
Claim(s) 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Austin et al; US 20210049668 A1 (hereinafter “Austin”) in view of Pankti Jayesh Kansara et al; US 20210117995 A1 (hereinafter “Kansara”) in further view of James et al; US 20210350385 A1 (hereinafter “James”) in further view of Abhinav; “Build a Recommendation Engine With Collaborative Filtering” (hereinafter “Abhinav”).
Regarding claim 6, Austin teaches a database, profile attributes of a user associated with the electronic data transfers for the one or more data records; (Austin Paragraph 0053; "In some embodiments, financial service provider device 130 may execute software instructions to search database 260 to retrieve information that matches the received user shopping data, which may include user-identifying information, user preference data, financial account information, prior purchase quantities, prior purchase prices, prior times of purchase, and prior total amounts spent, consistent with disclosed embodiments." Examiner notes that the database contains profile attributes of a user associated with the electronic data transfers for the one or more data records)
Austin does not teach The system of claim 5, wherein presenting the visual insight icons further comprises the instructions configuring the system to: dynamically generate content for the visual insight icons further based on: extracting, from a database
determine, from the database, similar users based on the profile attributes of the user;
grouping the similar users into clusters on the database;
and responsive to said grouping, generate similar content for the visual insight icons for the similar users based on historical knowledge of insights having a positive rating by prior users.
However, Abhinav does teach The system of claim 5, wherein presenting the visual insight icons further comprises the instructions configuring the system to: dynamically generate content for the visual insight icons further based on: extracting, from a database (Abhinav Section "The Dataset" Paragraph 1; "To experiment with recommendation algorithms, you’ll need data that contains a set of items and a set of users who have reacted to some of the items." Abhinav Section "Algorithms Based on K-Nearest Neighbors"' Paragraph 7; "The algorithm predicted that the user E would rate the movie 4.15, which could be high enough to be shown as a recommendation." Examiner notes that the content/highly rated movie is dynamically generated/changes with more data for the visual insight icons further extracting data from a database/dataset)
determine, from the database, similar users based on the profile attributes of the user; (Abhinav Section "How to Find Similar Users on the Basis of Ratings" shows determining similar users based on the profile attributes/ratings of the user)
grouping the similar users into clusters on the database; (Abhinav Section "What is Collaborative Filtering" Paragraph 2; "It works by searching a large group of people and finding a smaller set of users with tastes similar to a particular user." Examiner notes that finding a smaller set of user with tastes similar is grouping the similar users into clusters on the database)
and responsive to said grouping, generate similar content for the visual insight icons for the similar users based on historical knowledge of insights having a positive rating by prior users. (Abhinav Section "Algorithms Based on K-Nearest Neighbors"' Paragraph 7; "The algorithm predicted that the user E would rate the movie 4.15, which could be high enough to be shown as a recommendation." Examiner notes that responsive to said grouping, generating similar content/movie for visual insight icons for the similar users/user E based on historical knowledge of insights having a positive rating by prior users/algorithm predicts a user rating of 4.15 based on historical knowledge of insights having positive rating)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara, James, and Abhinav. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. Abhinav teaches collaborative filtering. One of ordinary skill would have motivation to combine Austin, Kansara, James, and Abhinav to use collaborative filtering to help recommenders to no overspecialize in a user’s profile “Collaborative filtering can help recommenders to not overspecialize in a user’s profile and recommend items that are completely different from what they have seen before.” (Abhinav Section “When Can Collaborative Filtering Be Used” Paragraph 1).
Regarding claim 17, Austin teaches a database, profile attributes of a user associated with the electronic data transfers for the one or more data records; (Austin Paragraph 0053; "In some embodiments, financial service provider device 130 may execute software instructions to search database 260 to retrieve information that matches the received user shopping data, which may include user-identifying information, user preference data, financial account information, prior purchase quantities, prior purchase prices, prior times of purchase, and prior total amounts spent, consistent with disclosed embodiments." Examiner notes that the database contains profile attributes of a user associated with the electronic data transfers for the one or more data records)
Austin does not teach The method of claim 16, wherein presenting the visual insight icons further comprises: dynamically generating content for the visual insight icons further based on: extracting, from a database
determining, from the database, similar users based on the profile attributes of the user;
grouping the similar users into clusters on the database;
and responsive to said grouping, generating similar content for the visual insight icons for the similar users based on historical knowledge of insights having a positive rating by prior users.
However, Abhinav does teach The method of claim 16, wherein presenting the visual insight icons further comprises: dynamically generating content for the visual insight icons further based on: extracting, from a database (Abhinav Section "The Dataset" Paragraph 1; "To experiment with recommendation algorithms, you’ll need data that contains a set of items and a set of users who have reacted to some of the items." Abhinav Section "Algorithms Based on K-Nearest Neighbors"' Paragraph 7; "The algorithm predicted that the user E would rate the movie 4.15, which could be high enough to be shown as a recommendation." Examiner notes that the content/highly rated movie is dynamically generated/changes with more data for the visual insight icons further extracting data from a database/dataset)
determining, from the database, similar users based on the profile attributes of the user; (Abhinav Section "How to Find Similar Users on the Basis of Ratings" shows determining similar users based on the profile attributes/ratings of the user)
grouping the similar users into clusters on the database; (Abhinav Section "What is Collaborative Filtering" Paragraph 2; "It works by searching a large group of people and finding a smaller set of users with tastes similar to a particular user." Examiner notes that finding a smaller set of user with tastes similar is grouping the similar users into clusters on the database)
and responsive to said grouping, generating similar content for the visual insight icons for the similar users based on historical knowledge of insights having a positive rating by prior users. (Abhinav Section "Algorithms Based on K-Nearest Neighbors"' Paragraph 7; "The algorithm predicted that the user E would rate the movie 4.15, which could be high enough to be shown as a recommendation." Examiner notes that responsive to said grouping, generating similar content/movie for visual insight icons for the similar users/user E based on historical knowledge of insights having a positive rating by prior users/algorithm predicts a user rating of 4.15 based on historical knowledge of insights having positive rating)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara, James, and Abhinav. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. Abhinav teaches collaborative filtering. One of ordinary skill would have motivation to combine Austin, Kansara, James, and Abhinav to use collaborative filtering to help recommenders to no overspecialize in a user’s profile “Collaborative filtering can help recommenders to not overspecialize in a user’s profile and recommend items that are completely different from what they have seen before.” (Abhinav Section “When Can Collaborative Filtering Be Used” Paragraph 1).
Claim(s) 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Austin et al; US 20210049668 A1 (hereinafter “Austin”) in view of Pankti Jayesh Kansara et al; US 20210117995 A1 (hereinafter “Kansara”) in further view of James et al; US 20210350385 A1 (hereinafter “James”) in further view of Abhinav; “Build a Recommendation Engine With Collaborative Filtering” (hereinafter “Abhinav”) in further view of Tom et al; US 20210073449 A1 (hereinafter “Tom”).
Regarding claim 7, Austin does not teach The system of claim 6, wherein the instructions further configure the system to: determine, from the predictive machine learning model, a degree of confidence related to the predicted future data transfers
However, James does teach The system of claim 6, wherein the instructions further configure the system to: determine, from the predictive machine learning model, a degree of confidence related to the predicted future data transfers (James Paragraph 0110; "the model may output a confidence score that reflects a likelihood that the output is accurate." Examiner notes that output is predicted future data transfers and a degree of confidence/confidence score is determined from the predictive machine learning model/model)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara and James. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. One of ordinary skill would have motivation to combine Austin, and Kansara and James to use user feedback to customer service goals “However, such summaries can be inaccurate and/or omit important details including details for which feedback would be most valuable (e.g., details enabling determination of causes of failure to achieve customer service goals).” (James Paragraph 0016).
Austin in view of James does not teach and retrieve a set of corresponding templates from the database for presenting the one or more interactive visual insight icons based on the degree of confidence wherein each of the templates is defined as associated with a range of degree of confidence.
However, Tom does teach and retrieve a set of corresponding templates from the database for presenting the one or more interactive visual insight icons based on the degree of confidence wherein each of the templates is defined as associated with a range of degree of confidence. (Tom Paragraph 0778; "The confidence level may be represented as a value in a range of values (e.g., 0-1, 1-10, 1-100, etc.), as a percentage (e.g., 0-100%), based on a textual rating (e.g., “very high,” “low,” etc.), as a symbolic or graphical representation (e.g. based on a color-based legend, predefined symbols, etc.), or by any other representation of varying degrees of confidence." Tom Paragraph 0862; "Resources may hold information in clear text (such as JSON objects, simple text files, XML files), binary data (such as pictures, videos, executables), or may be entries in databases." Tom Paragraph 0957; "Users may access and interact with reports 3019 using dashboard 3001 (e.g., by retrieving and displaying data from reports 3019)." Examiner notes that representations/templates are stored within a database; a set of corresponding templates is retrieved from the database for presenting the one or more interactive visual insight icons/representations populated to show data based on the degree of confidence/confidence level where in each of the templates is defined as associated with a range of degree confidence/may be represented as a value, percentage, or symbol of varying degrees of confidence)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara, James, Abhinav, and Tom. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. Abhinav teaches collaborative filtering. Tom teaches displaying confidence scores to users on a dashboard. One of ordinary skill would have motivation to combine Austin, Kansara, James, Abhinav, and Tom to use confidence level to help determine appropriate actions “the confidence levels may be compared to a confidence threshold to determine appropriate actions.” (Tom Paragraph 0778).
Regarding claim 18, Austin does not teach The method of claim 17 further comprising: determining, from the predictive machine learning model, a degree of confidence related to the predicted future data transfers
However, James does teach The method of claim 17 further comprising: determining, from the predictive machine learning model, a degree of confidence related to the predicted future data transfers (James Paragraph 0110; "the model may output a confidence score that reflects a likelihood that the output is accurate." Examiner notes that output is predicted future data transfers and a degree of confidence/confidence score is determined from the predictive machine learning model/model)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara and James. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. One of ordinary skill would have motivation to combine Austin, and Kansara and James to use user feedback to customer service goals “However, such summaries can be inaccurate and/or omit important details including details for which feedback would be most valuable (e.g., details enabling determination of causes of failure to achieve customer service goals).” (James Paragraph 0016).
Austin in view of James does not teach and retrieving a set of corresponding templates from the database for presenting the one or more interactive visual insight icons based on the degree of confidence wherein each of the templates is defined as associated with a range of degree of confidence.
However, Tom does teach and retrieving a set of corresponding templates from the database for presenting the one or more interactive visual insight icons based on the degree of confidence wherein each of the templates is defined as associated with a range of degree of confidence. (Tom Paragraph 0778; "The confidence level may be represented as a value in a range of values (e.g., 0-1, 1-10, 1-100, etc.), as a percentage (e.g., 0-100%), based on a textual rating (e.g., “very high,” “low,” etc.), as a symbolic or graphical representation (e.g. based on a color-based legend, predefined symbols, etc.), or by any other representation of varying degrees of confidence." Tom Paragraph 0862; "Resources may hold information in clear text (such as JSON objects, simple text files, XML files), binary data (such as pictures, videos, executables), or may be entries in databases." Tom Paragraph 0957; "Users may access and interact with reports 3019 using dashboard 3001 (e.g., by retrieving and displaying data from reports 3019)." Examiner notes that representations/templates are stored within a database; a set of corresponding templates is retrieved from the database for presenting the one or more interactive visual insight icons/representations populated to show data based on the degree of confidence/confidence level where in each of the templates is defined as associated with a range of degree confidence/may be represented as a value, percentage, or symbol of varying degrees of confidence)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara, James, Abhinav, and Tom. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. Abhinav teaches collaborative filtering. Tom teaches displaying confidence scores to users on a dashboard. One of ordinary skill would have motivation to combine Austin, Kansara, James, Abhinav, and Tom to use confidence level to help determine appropriate actions “the confidence levels may be compared to a confidence threshold to determine appropriate actions.” (Tom Paragraph 0778).
Claim(s) 11 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Austin et al; US 20210049668 A1 (hereinafter “Austin”) in view of James et al; US 20210350385 A1 (hereinafter “James”) in further view of Tom et al; US 20210073449 A1 (hereinafter “Tom”).
Regarding claim 11, Austin does not teach The system of claim 1, wherein the instructions further configure the system to: determine a confidence score for the set of factors likely to influence the predicted future data transfers;
However, James does teach The system of claim 1, wherein the instructions further configure the system to: determine a confidence score for the set of factors likely to influence the predicted future data transfers; (James Paragraph 0110; "the model may output a confidence score that reflects a likelihood that the output is accurate." Examiner notes that output is set of factors and a degree of confidence/confidence score is determined from the predictive machine learning model/model)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara and James. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. One of ordinary skill would have motivation to combine Austin, and Kansara and James to use user feedback to customer service goals “However, such summaries can be inaccurate and/or omit important details including details for which feedback would be most valuable (e.g., details enabling determination of causes of failure to achieve customer service goals).” (James Paragraph 0016).
Austin in view of James does not teach and dynamically manage and adjust a presentation of insights presented in the one or more interactive visual insight icons on the graphical user interface based on the determined confidence score of the set of factors.
However, Tom does teach and dynamically manage and adjust a presentation of insights presented in the one or more interactive visual insight icons on the graphical user interface based on the determined confidence score of the set of factors. (Tom Paragraph 0586; "The user may interact with the user interface to manually define associations between rooms and functional requirements." Tom Paragraph 0778; "The confidence level may be represented as a value in a range of values (e.g., 0-1, 1-10, 1-100, etc.), as a percentage (e.g., 0-100%), based on a textual rating (e.g., “very high,” “low,” etc.), as a symbolic or graphical representation (e.g. based on a color-based legend, predefined symbols, etc.), or by any other representation of varying degrees of confidence." Tom Paragraph 0957; "Users may access and interact with reports 3019 using dashboard 3001 (e.g., by retrieving and displaying data from reports 3019)." Examiner notes that as user interacts with associations between rooms and functional requirements, the dashboard is dynamically managing and adjusting a presentation insights presented in the one or more interactive visual insight icons/confidence level representations on the graphical user interface based on the determined confidence score of the set of factors/confidence level)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara, James, Abhinav, and Tom. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. Abhinav teaches collaborative filtering. Tom teaches displaying confidence scores to users on a dashboard. One of ordinary skill would have motivation to combine Austin, Kansara, James, Abhinav, and Tom to use confidence level to help determine appropriate actions “the confidence levels may be compared to a confidence threshold to determine appropriate actions.” (Tom Paragraph 0778).
Regarding claim 22, Austin does not teach The method of claim 12, further comprising: determining a confidence score for the set of factors likely to influence the predicted future data transfers;
However, James does teach The method of claim 12, further comprising: determining a confidence score for the set of factors likely to influence the predicted future data transfers; (James Paragraph 0110; "the model may output a confidence score that reflects a likelihood that the output is accurate." Examiner notes that output is set of factors and a degree of confidence/confidence score is determined from the predictive machine learning model/model)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara and James. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. One of ordinary skill would have motivation to combine Austin, and Kansara and James to use user feedback to customer service goals “However, such summaries can be inaccurate and/or omit important details including details for which feedback would be most valuable (e.g., details enabling determination of causes of failure to achieve customer service goals).” (James Paragraph 0016).
Austin in view of James does not teach and dynamically managing and adjusting a presentation of insights presented in the one or more interactive visual insight icons on the graphical user interface based on the determined confidence score of the set of factors.
However, Tom does teach and dynamically managing and adjusting a presentation of insights presented in the one or more interactive visual insight icons on the graphical user interface based on the determined confidence score of the set of factors. (Tom Paragraph 0586; "The user may interact with the user interface to manually define associations between rooms and functional requirements." Tom Paragraph 0778; "The confidence level may be represented as a value in a range of values (e.g., 0-1, 1-10, 1-100, etc.), as a percentage (e.g., 0-100%), based on a textual rating (e.g., “very high,” “low,” etc.), as a symbolic or graphical representation (e.g. based on a color-based legend, predefined symbols, etc.), or by any other representation of varying degrees of confidence." Tom Paragraph 0957; "Users may access and interact with reports 3019 using dashboard 3001 (e.g., by retrieving and displaying data from reports 3019)." Examiner notes that as user interacts with associations between rooms and functional requirements, the dashboard is dynamically managing and adjusting a presentation insights presented in the one or more interactive visual insight icons/confidence level representations on the graphical user interface based on the determined confidence score of the set of factors/confidence level)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Austin, Kansara, James, Abhinav, and Tom. Austin teaches a system for providing spending recommendations to a user. Kansara teaches a method for proactively predicting demand based on sparse transaction data. James teaches taking user feedback. Abhinav teaches collaborative filtering. Tom teaches displaying confidence scores to users on a dashboard. One of ordinary skill would have motivation to combine Austin, Kansara, James, Abhinav, and Tom to use confidence level to help determine appropriate actions “the confidence levels may be compared to a confidence threshold to determine appropriate actions.” (Tom Paragraph 0778).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/D.D.T./Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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